Skip to content
#explainable ai Open access

AI -Augmented HRM: transforming talent acquisition and employee experience in 2026.

Sep 2026 · Canadian Journal of Marketing Research · Vol 16, pp. 583-590

Abstract

Artificial Intelligence (AI) is becoming increasingly usual in HR management, especially in talent acquisition and AI-assisted decision making. But, in addition to the technological capacities, the successful application of AI needs HR professionals to understand and evaluate the recommendations of AI. In the present research, the impact of AI literacy and Explainable Artificial Intelligence (XAI) on AI-augmented talent acquisition is inves-tigated. Quantitative analysis was performed using 410 valid observations of HR managers interacting with AI recruitment systems under various conditions of XAI. Subjective perception of AI systems and objective understanding of AI recommendations in relation to Important Features, Counterfactuals and Model Criteria explanations were considered. It has been established that all three XAI methods increase subjective percep-tion significantly. Nevertheless, there is a huge difference in the way they affect objective understanding. Important Features greatly increase objective understanding, Counterfactuals do not matter at all and Model Criteria lead to a degradation of objective under-standing. It was found out that there is a positive relationship between AI literacy and objective understanding. The results of the research demonstrate that there should be balance between AI literacy, explanation design and human judgement for effective AI augmented HRM.

View source

Similar papers

#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 64 citations · ⚡6
#computer vision Review Apr 2024

AI-powered Code Review with LLMs: Early Results

The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.

Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al. · 62 citations · ⚡3
#computer vision Open access Mar 2024

LLM-based agents for automating the enhancement of user story quality: An early report

The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.

Zheying Zhang, M. Rayhan, Tomas Herda et al. · 48 citations · ⚡4
#computer vision Review Mar 2024

System for systematic literature review using multiple AI agents: Concept and an empirical evaluation

This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.

Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al. · 44 citations · ⚡2
#computer vision Feb 2024

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.

Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al. · 41 citations
#artificial intelligence Conference Open access Jun 2018

The Key Concepts of Ethics of Artificial Intelligence

It is suggested that the focus on finding keywords is the first step in guiding and providing direction for future research in the AI ethics field.

Ville Vakkuri, P. Abrahamsson · 39 citations · ⚡2

Related blog posts

Microsoft Research Blog Oct 12, 2026

Simulating student learning for better AI tutoring

StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.

GPT-Lab Oct 12, 2026

AI Has Grown Up. Have We?

AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post AI Has Grown Up. Have We? appeared first on GPT-Lab.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.